发表机构
National University of Singapore; The Hong Kong Polytechnic University（新加坡国立大学; 香港理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出Segment-Snap，通过几何与语义耦合预测部件和把手，利用几何解码器约束运动并选择铰链线，在Articulate3D上显著提升交互理解性能。
AI 中文摘要
三维场景中的交互理解需要对可移动部件、它们的运动以及可操作区域进行联合描述。我们提出了Segment-Snap，它通过部件与把手之间的物理关系将这些输出连接起来。学习到的预测器识别广泛的部件表面和小型把手。几何解码器利用平面和直立先验约束运动，然后根据预测的把手位置选择铰链线，无需训练运动回归器。相反，联合部件与把手预测器提供额外的把手候选，其运动类别通过包含部件进行细化。每次信息传递仅应用一次，无迭代反馈。在Articulate3D验证集上，把手引导在固定掩码和轴下将运动门控AP从13.74%提升至40.98%。额外的把手候选将把手AP从24.63%提升至29.65%；基于部件的类别修正增加0.98个百分点，完整上下文达到30.99%。重复训练、学习解码器控制和配对可视化确立了结合几何与语义证据进行交互理解的益处与局限。
英文摘要
Understanding interaction in a 3D scene requires recovering movable parts, their motion, and where they can be operated. These quantities are related, and their predictions can inform one another. A closed cabinet door, for instance, reveals a movable surface but may leave the hinge side ambiguous; its handle helps resolve this ambiguity, while the part provides context for localizing and interpreting the small handle. Building on this observation, we present SEGMENT-SNAP, which combines geometric and semantic evidence through part-handle coupling. Three independently trained predictors recover movable parts, dense handles, and part-associated handle proposals. We couple their outputs in two directions. For part motion, a training-free geometric decoder fits predicted part surfaces under explicit physical priors and uses detected handles to select candidate hinge lines. For handle prediction, a part-conditioned branch proposes additional handles, while standalone part classes refine their rotation/translation labels, with dense-handle labels as a fallback. Each transfer is applied once, without iterative feedback. On the Articulate3D validation set, handle guidance raises motion-gated AP from 13.74 to 40.98 under fixed masks and axes. Additional handle proposals raise handle AP from 24.63 to 29.65, and full contextual class correction raises it to 30.99 in the reference configuration. Fixed-input controls, retraining ablations, learned-decoder comparisons, and paired visualizations together characterize the benefits and limits of this coupling. Our system also achieved first place in the Articulate3D Challenge.
CommentsProject page: https://hyokong.github.io/segment-snap-page/
